Quantitative motion detection of in-hand objects for robotic grasp manipulation

Author:

Li Xueyong1ORCID,Zhao Kai1,Lu Changhou1,Wang Yonghui2

Affiliation:

1. Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China

2. Department of Physical Medicine and Rehabilitation, Qilu Hospital, Shandong University, Jinan, China

Abstract

In an actual grasp operation, grasp accidents (slant, rolling, turning over, and dropping) of in-hand objects occur frequently. Quantitative motion detection of in-hand objects is critical to optimize the grasp configuration and to improve the stability and dexterity of a grasp manipulation. In this article, an innovative method for quantitative measurement of the motions of in-hand objects is presented. Firstly, the slip information at object–finger interface between adjacent states is detected by three omnidirectional slip sensors; next, singular value decomposition method is applied to calculate the rotation and translation matrices according to the relative coordinate changes extracted from the slip information. Finally, Euler angles and the linear displacements which illustrate the motion of in-hand objects are quantitatively measured from the translation matrix and the rotation matrix, and the continuous motion track can be further established. Experiment results show that the proposed method is effective in detecting multiple motion information of in-hand objects.

Funder

Fundamental Research Funds of Shandong University

Publisher

SAGE Publications

Subject

Artificial Intelligence,Computer Science Applications,Software

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